Energy-Efficient Aerial IRS Configuration and Resource Allocation in AoI-Aware MEC

In this paper, we investigate task offloading and computing in an urban mobile edge computing (MEC) system assisted by an aerial intelligent reflecting surface (AIRS). To enhance information freshness while saving energy, we formulate a joint optimization problem to minimize the weighted sum of average age of information (AoI) and total energy consumption by jointly optimizing task offloading decisions, resource allocation, and AIRS configuration including its deployment position, phase shifts, and panel size, subject to offloading quality and computing deadline constraints. To tackle the resulting mixed-integer and nonconvex problem, we develop a hierarchical optimization framework based on the objective priority and variable coupling relations. Under this framework, the problem is solved in two stages using quadratic penalty, numerical analysis, and convex optimization techniques. Specifically, an AoI-aware task offloading policy is first designed to maximize information freshness; subsequently, given the obtained offloading policy, an AIRS configuration and resource allocation scheme is proposed to minimize energy consumption. Simulation results demonstrate that the proposed approach significantly outperforms benchmark schemes in reducing both AoI and energy consumption, while exhibiting superior convergence performance.